DEPLOY.md

Deploy — Disfluency Remover Space

STATUS: NOT PUBLISHED. Everything below is a deliberate, outward-facing publishing action and requires the user's explicit go-ahead. The local space/ artifacts are committed; nothing has been pushed to the Hub.

Prerequisites

1. Push the v1 adapter to the Hub (REQUIRED — Space can't load without it)

space/app.py loads ADAPTER = "pradachan/whisper-large-v3-turbo-disfluency-lora". That Hub repo does not exist yet — the v1 adapter lives locally at /models/whisper-lora-disfluency. Push it first:

# from a machine with the adapter + huggingface_hub installed and logged in
huggingface-cli login   # or: export HF_TOKEN=hf_...
huggingface-cli upload \
  pradachan/whisper-large-v3-turbo-disfluency-lora \
  /models/whisper-lora-disfluency . \
  --repo-type=model

If you prefer to keep the adapter private at skeleton stage, create it private and add the token as a Space secret (step 4) — the Space will then authenticate to pull it.

2. Export gallery audio clips (so example chips are clickable)

The precomputed gallery text is already baked into app.py. To make the example clips playable, export the matching test-set audio by idx:

# run once; writes space/examples/idx_XXX.wav
import soundfile as sf
from datasets import load_dataset, Audio

ds = load_dataset("amaai-lab/DisfluencySpeech", split="test", trust_remote_code=True)
ds = ds.cast_column("audio", Audio(sampling_rate=16000))
for idx in (1, 125, 43, 248):
    a = ds[idx]["audio"]
    sf.write(f"space/examples/idx_{idx:03d}.wav", a["array"], a["sampling_rate"])

app.py only registers gr.Examples for clips that exist on disk, so the app runs fine with or without this step.

Create + push the Space

# one-time: create the Space (gradio SDK, ZeroGPU hardware) under the user's account
huggingface-cli repo create disfluency-remover --type space --space_sdk gradio

# push the contents of space/ to the Space repo root
cd space
git init && git remote add origin https://huggingface.co/spaces/pradachan/disfluency-remover
git add app.py requirements.txt README.md examples/ 2>/dev/null
git commit -m "Disfluency Remover skeleton (v1 adapter)"
git push origin main      # use an HF token / git credential helper

(Alternatively pip install huggingface_hub and use HfApi().upload_folder(folder_path="space", repo_id="pradachan/disfluency-remover", repo_type="space").)

3. Set ZeroGPU hardware

README.md already declares hardware: zero-gpu in the YAML header. Confirm in the Space Settings → Hardware that ZeroGPU is selected after the first push.

4. Set the Space secret (only if the adapter repo is private)

Space Settings → Variables and secrets → New secret:

• Name: HF_TOKEN
• Value: a token with read access to the private adapter repo.

The transformers/peft loaders read HF_TOKEN automatically.

Live verification (after the Space is up)

• Mic clip: record ~10s saying "you know" / "I mean" / a repeated word → the Cleaned pane drops them and the diff pane shows red strikethroughs. Warm latency should be < 15s.
• Chunking: upload a ~60s clip → it is split into 30s windows and the texts are concatenated; output should be coherent end to end.
• Phone-number caveat: upload a clip containing a spoken number sequence and confirm whether digits collapse. If they do, keep such an example out of the gallery and document it in the limitations note (and as the Epic 08 "honest failure" example at final stage).
• Cold start: first request after idle is ~30s (model load on ZeroGPU). The baked-in gallery text keeps the page demonstrable during that window.

Final-stage swap (Epic 07/09 — NOT now)

• Point ADAPTER at the winner adapter Hub repo.
• Curate gallery to 3 wins + 1 self-repair + 1 honest failure (add the Epic 08 failure to GALLERY in app.py with a "limitation" label).
• Update README claim numbers to match Epic 09 claims rules.

Decision gate

• ZeroGPU quota/queue problems on demo day → fall back to a paid T4 Space (hardware: t4-small, ~$0.60/h). Decide by Jun 14 evening; do not debug live.
• If the winner adapter isn't ready by Jun 14 evening, ship with v1 permanently and update the card numbers accordingly (v1 beats vanilla; demo works).